{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.218658Z",
     "start_time": "2025-10-28T05:24:11.288116Z"
    }
   },
   "source": [
    "#导包\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.546933Z",
     "start_time": "2025-10-28T05:24:14.485319Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#导入MNIST数据集\n",
    "train_dataset = torchvision.datasets.MNIST(root='', train=True, transform=None, target_transform=None, download=True)\n",
    "test_dataset = torchvision.datasets.MNIST(root='', train=False, transform=None, target_transform=None, download=True)"
   ],
   "id": "59077884db90738f",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.593676Z",
     "start_time": "2025-10-28T05:24:14.580209Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#查看数据集\n",
    "train_dataset"
   ],
   "id": "97d470d277e1d395",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 60000\n",
       "    Root location: \n",
       "    Split: Train"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.640235Z",
     "start_time": "2025-10-28T05:24:14.627093Z"
    }
   },
   "cell_type": "code",
   "source": "train_dataset.data",
   "id": "f7d6bceee036dddc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]]], dtype=torch.uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.717361Z",
     "start_time": "2025-10-28T05:24:14.705739Z"
    }
   },
   "cell_type": "code",
   "source": "train_dataset.targets",
   "id": "a3f18887242fff32",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5, 0, 4,  ..., 5, 6, 8])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.780591Z",
     "start_time": "2025-10-28T05:24:14.766582Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#创建字典容器\n",
    "dict =  {}\n",
    "for i in range(10):\n",
    "   key = train_dataset.classes[i].split(' ')[2]\n",
    "   dict[key] = i\n",
    "print(dict)"
   ],
   "id": "370ba8cb43d7bbe4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zero': 0, 'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 'six': 6, 'seven': 7, 'eight': 8, 'nine': 9}\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.811312Z",
     "start_time": "2025-10-28T05:24:14.798031Z"
    }
   },
   "cell_type": "code",
   "source": "len(train_dataset.data)",
   "id": "6fe6f29e22b5f96a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:14.874002Z",
     "start_time": "2025-10-28T05:24:14.828919Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#读取前十个张量照片\n",
    "for i in range(10):\n",
    "    print(train_dataset.data[i])"
   ],
   "id": "bfc6815e34af83ca",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,  67, 232,  39,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,  62,  81,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0, 120, 180,  39,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0, 126, 163,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   2, 153, 210,  40,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0, 220, 163,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,  27, 254, 162,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0, 222, 163,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0, 183, 254, 125,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,  46, 245, 163,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0, 198, 254,  56,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0, 120, 254, 163,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,  23, 231, 254,  29,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0, 159, 254, 120,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0, 163, 254, 216,  16,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0, 159, 254,  67,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,  14,  86, 178, 248, 254,  91,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0, 159, 254,  85,   0,   0,   0,  47,  49, 116, 144, 150,\n",
      "         241, 243, 234, 179, 241, 252,  40,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0, 150, 253, 237, 207, 207, 207, 253, 254, 250, 240, 198,\n",
      "         143,  91,  28,   5, 233, 250,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0, 119, 177, 177, 177, 177, 177,  98,  56,   0,   0,\n",
      "           0,   0,   0, 102, 254, 220,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 254, 137,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 254,  57,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 254,  57,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 255,  94,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 254,  96,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 254, 153,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 169, 255, 153,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,  96, 254, 153,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0, 124, 253, 255,  63,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,  96, 244, 251, 253,  62,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 127, 251, 251, 253,  62,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,  68, 236, 251, 211,  31,   8,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,  60, 228, 251, 251,  94,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0, 155, 253, 253, 189,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          20, 253, 251, 235,  66,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  32,\n",
      "         205, 253, 251, 126,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 104,\n",
      "         251, 253, 184,  15,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  80, 240,\n",
      "         251, 193,  23,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  32, 253, 253,\n",
      "         253, 159,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 151, 251, 251,\n",
      "         251,  39,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  48, 221, 251, 251,\n",
      "         172,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 234, 251, 251, 196,\n",
      "          12,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 253, 251, 251,  89,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0, 159, 255, 253, 253,  31,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,  48, 228, 253, 247, 140,   8,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,  64, 251, 253, 220,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,  64, 251, 253, 220,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,  24, 193, 253, 220,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  55, 148,\n",
      "         210, 253, 253, 113,  87, 148,  55,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  87, 232, 252,\n",
      "         253, 189, 210, 252, 252, 253, 168,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   4,  57, 242, 252, 190,\n",
      "          65,   5,  12, 182, 252, 253, 116,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  96, 252, 252, 183,  14,\n",
      "           0,   0,  92, 252, 252, 225,  21,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0, 132, 253, 252, 146,  14,   0,\n",
      "           0,   0, 215, 252, 252,  79,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0, 126, 253, 247, 176,   9,   0,   0,\n",
      "           8,  78, 245, 253, 129,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,  16, 232, 252, 176,   0,   0,   0,  36,\n",
      "         201, 252, 252, 169,  11,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,  22, 252, 252,  30,  22, 119, 197, 241,\n",
      "         253, 252, 251,  77,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,  16, 231, 252, 253, 252, 252, 252, 226,\n",
      "         227, 252, 231,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,  55, 235, 253, 217, 138,  42,  24,\n",
      "         192, 252, 143,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  62,\n",
      "         255, 253, 109,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  71,\n",
      "         253, 252,  21,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         253, 252,  21,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  71,\n",
      "         253, 252,  21,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 106,\n",
      "         253, 252,  21,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  45,\n",
      "         255, 253,  21,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         218, 252,  56,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          96, 252, 189,  42,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          14, 184, 252, 170,  11,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,  14, 147, 252,  42,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,  13,  25, 100, 122,   7,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  33,\n",
      "         151, 208, 252, 252, 252, 146,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  40, 152, 244,\n",
      "         252, 253, 224, 211, 252, 232,  40,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  15, 152, 239, 252, 252,\n",
      "         252, 216,  31,  37, 252, 252,  60,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  96, 252, 252, 252, 252,\n",
      "         217,  29,   0,  37, 252, 252,  60,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0, 181, 252, 252, 220, 167,\n",
      "          30,   0,   0,  77, 252, 252,  60,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  26, 128,  58,  22,   0,\n",
      "           0,   0,   0, 100, 252, 252,  60,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0, 157, 252, 252,  60,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 110,\n",
      "         121, 122, 121, 202, 252, 194,   3,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  10,  53, 179, 253,\n",
      "         253, 255, 253, 253, 228,  35,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   5,  54, 227, 252, 243, 228,\n",
      "         170, 242, 252, 252, 231, 117,   6,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   6,  78, 252, 252, 125,  59,   0,\n",
      "          18, 208, 252, 252, 252, 252,  87,   7,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   5, 135, 252, 252, 180,  16,   0,  21,\n",
      "         203, 253, 247, 129, 173, 252, 252, 184,  66,  49,  49,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   3, 136, 252, 241, 106,  17,   0,  53, 200,\n",
      "         252, 216,  65,   0,  14,  72, 163, 241, 252, 252, 223,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0, 105, 252, 242,  88,  18,  73, 170, 244, 252,\n",
      "         126,  29,   0,   0,   0,   0,   0,  89, 180, 180,  37,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0, 231, 252, 245, 205, 216, 252, 252, 252, 124,\n",
      "           3,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0, 207, 252, 252, 252, 252, 178, 116,  36,   4,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,  13,  93, 143, 121,  23,   6,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 145, 255,\n",
      "         211,  31,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  32, 237, 253,\n",
      "         252,  71,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  11, 175, 253,\n",
      "         252,  71,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 144, 253,\n",
      "         252,  71,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  16, 191, 253,\n",
      "         252,  71,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  26, 221, 253,\n",
      "         252, 124,  31,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 125, 253,\n",
      "         252, 252, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 253,\n",
      "         252, 252, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,\n",
      "         253, 253, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 253,\n",
      "         252, 252, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 253,\n",
      "         252, 252, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 253,\n",
      "         252, 252, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,\n",
      "         253, 253, 170,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 253,\n",
      "         252, 252, 252,  42,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 149,\n",
      "         252, 252, 252, 144,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 109,\n",
      "         252, 252, 252, 144,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         218, 253, 253, 255,  35,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         175, 252, 252, 253,  35,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          73, 252, 252, 253,  35,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          31, 211, 252, 253,  35,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  38,  43, 105,\n",
      "         255, 253, 253, 253, 253, 253, 174,   6,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  43, 139, 224, 226, 252,\n",
      "         253, 252, 252, 252, 252, 252, 252, 158,  14,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0, 178, 252, 252, 252, 252,\n",
      "         253, 252, 252, 252, 252, 252, 252, 252,  59,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0, 109, 252, 252, 230, 132,\n",
      "         133, 132, 132, 189, 252, 252, 252, 252,  59,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   4,  29,  29,  24,   0,\n",
      "           0,   0,   0,  14, 226, 252, 252, 172,   7,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,  85, 243, 252, 252, 144,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,  88, 189, 252, 252, 252,  14,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          91, 212, 247, 252, 252, 252, 204,   9,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  32, 125, 193, 193, 193,\n",
      "         253, 252, 252, 252, 238, 102,  28,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,  45, 222, 252, 252, 252, 252,\n",
      "         253, 252, 252, 252, 177,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,  45, 223, 253, 253, 253, 253,\n",
      "         255, 253, 253, 253, 253,  74,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  31, 123,  52,  44,  44,\n",
      "          44,  44, 143, 252, 252,  74,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,  15, 252, 252,  74,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,  86, 252, 252,  74,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   5,  75,   9,   0,   0,   0,   0,   0,\n",
      "           0,  98, 242, 252, 252,  74,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,  61, 183, 252,  29,   0,   0,   0,   0,  18,\n",
      "          92, 239, 252, 252, 243,  65,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0, 208, 252, 252, 147, 134, 134, 134, 134, 203,\n",
      "         253, 252, 252, 188,  83,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0, 208, 252, 252, 252, 252, 252, 252, 252, 252,\n",
      "         253, 230, 153,   8,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,  49, 157, 252, 252, 252, 252, 252, 217, 207,\n",
      "         146,  45,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   7, 103, 235, 252, 172, 103,  24,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n",
      "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
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      "         230,  24,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  20, 254,\n",
      "         254,  48,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  20, 254,\n",
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      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  20, 254,\n",
      "         254,  57,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  20, 254,\n",
      "         254, 108,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  16, 239,\n",
      "         254, 143,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
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      "         254, 143,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 178,\n",
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      "       dtype=torch.uint8)\n",
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      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "       dtype=torch.uint8)\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:15.402305Z",
     "start_time": "2025-10-28T05:24:14.891783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#显示前3个张量图片\n",
    "for i in range(3):\n",
    "    image, label = train_dataset[i]  # 修复：解包元组获取图像和标签\n",
    "    plt.imshow(image, cmap='gray')   # 修复：使用图像数据并添加灰度映射\n",
    "    plt.title(f'Label: {label}')     # 添加标签标题\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 显示前3个张量图片\n",
    "for i in range(3):\n",
    "    plt.imshow(train_dataset.data[i], cmap='gray')\n",
    "    plt.show()"
   ],
   "id": "174972fd6186d40f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaEAAAGxCAYAAADLfglZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfY0lEQVR4nO3de3BU9f3/8ddyWxGTnUZIsuESUwq1cq2AXL5WLo4ZYqEg2iJ2BGzHwXKpFNGKoAQvxKHKaKXgpYpQoGRaEbFSMTYQbIFOQByRWgfGUMJATEnpbgyQFPj8/mDYn2tC4Cy7vHN5PmY+M+w5573nncMhLz67Z8/6nHNOAAAYaGHdAACg+SKEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYTQLL3++uvy+XzauXNnXJ7P5/Np+vTpcXmurz5nbm5uzPX/+9//tGDBAl1zzTXy+/269tpr9cILL8SvQSAOWlk3ACAxpk6dqt/97nd64oknNGDAAG3atEn333+/Kisr9cgjj1i3B0gihIAmae/evXr11Vf11FNP6cEHH5QkDRs2TBUVFXryySd13333KSUlxbhLgJfjgPM6efKkHnjgAfXt21eBQEApKSkaPHiw3nrrrfPWvPTSS+revbv8fr+uu+46rV27ttY2ZWVlmjJlijp16qQ2bdooKytLCxYs0KlTp+LW+/r16+Wc0z333BO1/J577tGJEyf07rvvxm1fwKVgJgScR3V1tf7zn/9o9uzZ6tixo2pqavT+++9r3LhxWr58uSZOnBi1/YYNG7R582Y9/vjjateunZYuXaoJEyaoVatWuuOOOySdDaAbbrhBLVq00GOPPaauXbtq+/btevLJJ3XgwAEtX7683p6uueYaSdKBAwfq3e6TTz5Rhw4dlJ6eHrW8d+/ekfVAQ0AIAecRCASiQuH06dO6+eabdezYMT333HO1Qujo0aMqLi5WWlqaJOnWW29Vz549NWfOnEgI5ebm6tixY9q7d6+6dOkiSbr55pvVtm1bzZ49Ww8++KCuu+668/bUqtXF/ZOtqKio8+W2du3aqU2bNqqoqLio5wESjZfjgHr84Q9/0P/93//pqquuUqtWrdS6dWu9+uqr+vTTT2tte/PNN0cCSJJatmyp8ePHa//+/Tp06JAk6U9/+pOGDx+ujIwMnTp1KjJycnIkSUVFRfX2s3//fu3fv/+ievf5fDGtAy4nQgg4j3Xr1ulHP/qROnbsqFWrVmn79u0qLi7WT37yE508ebLW9l9/6eury87NPL744gu9/fbbat26ddTo0aOHpLOzqXi4+uqr65ztVFVVqaamhosS0GDwchxwHqtWrVJWVpby8/OjZg7V1dV1bl9WVnbeZVdffbUkqX379urdu7eeeuqpOp8jIyPjUtuWJPXq1Utr165VWVlZVDju2bNHktSzZ8+47Ae4VMyEgPPw+Xxq06ZNVACVlZWd9+q4v/zlL/riiy8ij0+fPq38/Hx17dpVnTp1kiSNGjVKn3zyibp27ar+/fvXGvEKoTFjxsjn82nFihVRy19//XW1bdtWI0eOjMt+gEvFTAjNWmFhYZ1Xmt16660aNWqU1q1bp6lTp+qOO+5QaWmpnnjiCQWDQe3bt69WTfv27TVixAg9+uijkavj/vnPf0Zdpv3444+roKBAQ4YM0c9//nN9+9vf1smTJ3XgwAFt3LhRL774YiSw6vKtb31Lki74vlCPHj3005/+VPPnz1fLli01YMAAvffee3r55Zf15JNP8nIcGgxCCM3aL3/5yzqXl5SU6J577lF5eblefPFFvfbaa/rmN7+phx9+WIcOHdKCBQtq1fzgBz9Qjx49NG/ePB08eFBdu3bV6tWrNX78+Mg2wWBQO3fu1BNPPKFf/epXOnTokJKSkpSVlaWRI0fqG9/4Rr39evks0dKlS9WxY0e98MILKisr0zXXXKPnn39eM2bMuOjnABLN55xz1k0AAJon3hMCAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYa3OeEzpw5o8OHDyspKYmbLAJAI+ScU2VlpTIyMtSiRf1znQYXQocPH1bnzp2t2wAAXKLS0tJ67wAiNcCX45KSkqxbAADEwcX8Pk9YCC1dulRZWVm64oor1K9fP33wwQcXVcdLcADQNFzM7/OEhFB+fr5mzpypuXPnavfu3fre976nnJwcHTx4MBG7AwA0Ugm5d9zAgQN1/fXXa9myZZFl3/nOdzR27Fjl5eXVWxsOhxUIBOLdEgDgMguFQkpOTq53m7jPhGpqarRr1y5lZ2dHLc/Ozta2bdtqbV9dXa1wOBw1AADNQ9xD6OjRozp9+rTS0tKilqelpdX5zZN5eXkKBAKRwZVxANB8JOzChK+/IeWcq/NNqjlz5igUCkVGaWlpoloCADQwcf+cUPv27dWyZctas57y8vJasyNJ8vv98vv98W4DANAIxH0m1KZNG/Xr108FBQVRy899pTEAAOck5I4Js2bN0t13363+/ftr8ODBevnll3Xw4EHdd999idgdAKCRSkgIjR8/XhUVFXr88cd15MgR9ezZUxs3blRmZmYidgcAaKQS8jmhS8HnhACgaTD5nBAAABeLEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmWlk3AODi9OvXz3PN9OnTY9rXxIkTPdesXLnSc80LL7zguebDDz/0XIOGi5kQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAMz7nnLNu4qvC4bACgYB1G0BC9e3b13NNYWGh55rk5GTPNZdTKBTyXHP11VcnoBMkQigUuuA5yEwIAGCGEAIAmIl7COXm5srn80WN9PT0eO8GANAEJORL7Xr06KH3338/8rhly5aJ2A0AoJFLSAi1atWK2Q8A4IIS8p7Qvn37lJGRoaysLN155536/PPPz7ttdXW1wuFw1AAANA9xD6GBAwdq5cqV2rRpk1555RWVlZVpyJAhqqioqHP7vLw8BQKByOjcuXO8WwIANFAJ/5xQVVWVunbtqoceekizZs2qtb66ulrV1dWRx+FwmCBCk8fnhM7ic0JN28V8Tigh7wl9Vbt27dSrVy/t27evzvV+v19+vz/RbQAAGqCEf06ourpan376qYLBYKJ3BQBoZOIeQrNnz1ZRUZFKSkr097//XXfccYfC4bAmTZoU710BABq5uL8cd+jQIU2YMEFHjx5Vhw4dNGjQIO3YsUOZmZnx3hUAoJHjBqbAJbrhhhs817zxxhueazIyMjzXxPrPu7Ky0nNNTU2N55pYLjK48cYbPdd8+OGHnmuk2H4m/H/cwBQA0KARQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwk/AvtQMsXHnllTHVXX/99Z5rVq1a5bmmoX+/1vm+hLI+ixYt8lyzdu1azzV/+9vfPNfMmzfPc40k5eXlxVSHi8dMCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghrtoo0l66aWXYqqbMGFCnDtpnGK5m/hVV13luaaoqMhzzbBhwzzX9O7d23MNLg9mQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMxwA1M0eP369fNc8/3vfz+mffl8vpjqvIrlxp1vv/2255pnnnnGc40kHT582HPN7t27PdccO3bMc82IESM811yuv1d4x0wIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGZ9zzlk38VXhcFiBQMC6DSRI3759PdcUFhZ6rklOTvZcE6s///nPnmsmTJjguWbo0KGea3r37u25RpJ++9vfeq7597//HdO+vDp9+rTnmuPHj8e0r1iO+YcffhjTvpqiUCh0wX+LzIQAAGYIIQCAGc8htHXrVo0ePVoZGRny+Xxav3591HrnnHJzc5WRkaG2bdtq2LBh2rt3b7z6BQA0IZ5DqKqqSn369NGSJUvqXL9o0SItXrxYS5YsUXFxsdLT03XLLbeosrLykpsFADQtnr9ZNScnRzk5OXWuc87pueee09y5czVu3DhJ0ooVK5SWlqY1a9ZoypQpl9YtAKBJiet7QiUlJSorK1N2dnZkmd/v19ChQ7Vt27Y6a6qrqxUOh6MGAKB5iGsIlZWVSZLS0tKilqelpUXWfV1eXp4CgUBkdO7cOZ4tAQAasIRcHefz+aIeO+dqLTtnzpw5CoVCkVFaWpqIlgAADZDn94Tqk56eLunsjCgYDEaWl5eX15odneP3++X3++PZBgCgkYjrTCgrK0vp6ekqKCiILKupqVFRUZGGDBkSz10BAJoAzzOhL7/8Uvv37488Likp0UcffaSUlBR16dJFM2fO1MKFC9WtWzd169ZNCxcu1JVXXqm77rorro0DABo/zyG0c+dODR8+PPJ41qxZkqRJkybp9ddf10MPPaQTJ05o6tSpOnbsmAYOHKj33ntPSUlJ8esaANAkcANTxKx79+6ea+bPn++55s477/Rcc/ToUc81knTkyBHPNU8++aTnmj/+8Y+ea3BWLDcwjfXXXH5+vueaH//4xzHtqyniBqYAgAaNEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGAmrt+sisYp1m+2feaZZzzX3HrrrZ5rKisrPddMnDjRc4109qtKvGrbtm1M+0LD16VLF+sWmjxmQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMxwA1Pou9/9bkx1sdyMNBZjxozxXFNUVJSATgDEGzMhAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZriBKbR48eKY6nw+n+eaWG4sys1I8VUtWnj/v/OZM2cS0AnigZkQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM9zAtIkZNWqU55q+ffvGtC/nnOeaDRs2xLQv4JxYbkYay7kqSR999FFMdbh4zIQAAGYIIQCAGc8htHXrVo0ePVoZGRny+Xxav3591PrJkyfL5/NFjUGDBsWrXwBAE+I5hKqqqtSnTx8tWbLkvNuMHDlSR44ciYyNGzdeUpMAgKbJ84UJOTk5ysnJqXcbv9+v9PT0mJsCADQPCXlPaMuWLUpNTVX37t117733qry8/LzbVldXKxwORw0AQPMQ9xDKycnR6tWrVVhYqGeffVbFxcUaMWKEqqur69w+Ly9PgUAgMjp37hzvlgAADVTcPyc0fvz4yJ979uyp/v37KzMzU++8847GjRtXa/s5c+Zo1qxZkcfhcJggAoBmIuEfVg0Gg8rMzNS+ffvqXO/3++X3+xPdBgCgAUr454QqKipUWlqqYDCY6F0BABoZzzOhL7/8Uvv37488Likp0UcffaSUlBSlpKQoNzdXt99+u4LBoA4cOKBHHnlE7du312233RbXxgEAjZ/nENq5c6eGDx8eeXzu/ZxJkyZp2bJl2rNnj1auXKn//ve/CgaDGj58uPLz85WUlBS/rgEATYLnEBo2bFi9NwPctGnTJTWES9O2bVvPNW3atIlpX/Vden8++fn5Me0LDV8s7+3m5ubGv5E6FBYWxlQ3Z86cOHeCr+PecQAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAMwn/ZlU0XdXV1Z5rjhw5koBOEG+x3BF73rx5nmsefPBBzzWHDh3yXPPss896rpHOfn8aEouZEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADPcwBQx27Bhg3ULuIC+ffvGVBfLjUXHjx/vueatt97yXHP77bd7rkHDxUwIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGW5g2sT4fL7LUiNJY8eO9Vxz//33x7QvSL/4xS881zz66KMx7SsQCHiuWb16teeaiRMneq5B08JMCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBluYNrEOOcuS40kpaene6759a9/7bnmtdde81xTUVHhuUaSBg0a5Lnm7rvv9lzTp08fzzWdOnXyXHPw4EHPNZK0adMmzzVLly6NaV9o3pgJAQDMEEIAADOeQigvL08DBgxQUlKSUlNTNXbsWH322WdR2zjnlJubq4yMDLVt21bDhg3T3r1749o0AKBp8BRCRUVFmjZtmnbs2KGCggKdOnVK2dnZqqqqimyzaNEiLV68WEuWLFFxcbHS09N1yy23qLKyMu7NAwAaN08XJrz77rtRj5cvX67U1FTt2rVLN910k5xzeu655zR37lyNGzdOkrRixQqlpaVpzZo1mjJlSvw6BwA0epf0nlAoFJIkpaSkSJJKSkpUVlam7OzsyDZ+v19Dhw7Vtm3b6nyO6upqhcPhqAEAaB5iDiHnnGbNmqUbb7xRPXv2lCSVlZVJktLS0qK2TUtLi6z7ury8PAUCgcjo3LlzrC0BABqZmENo+vTp+vjjj/X73/++1jqfzxf12DlXa9k5c+bMUSgUiozS0tJYWwIANDIxfVh1xowZ2rBhg7Zu3Rr1AbpzH14sKytTMBiMLC8vL681OzrH7/fL7/fH0gYAoJHzNBNyzmn69Olat26dCgsLlZWVFbU+KytL6enpKigoiCyrqalRUVGRhgwZEp+OAQBNhqeZ0LRp07RmzRq99dZbSkpKirzPEwgE1LZtW/l8Ps2cOVMLFy5Ut27d1K1bNy1cuFBXXnml7rrrroT8AACAxstTCC1btkySNGzYsKjly5cv1+TJkyVJDz30kE6cOKGpU6fq2LFjGjhwoN577z0lJSXFpWEAQNPhc7HevTJBwuGwAoGAdRuN1g9/+EPPNXVdXNKQfPHFF55rYr3Uv1u3bjHVXQ7bt2/3XLN58+aY9vXYY4/FVAd8VSgUUnJycr3bcO84AIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAICZmL5ZFQ1XLHdaLi4ujmlfAwYMiKnOq3Pf2OvF+b7JNxEqKio816xdu9Zzzf333++5BmjomAkBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAw43POOesmviocDisQCFi30awEg8GY6qZMmeK5Zt68eZ5rfD6f55pYT+vnn3/ec82yZcs81+zfv99zDdDYhEIhJScn17sNMyEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmuIEpACAhuIEpAKBBI4QAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGU8hlJeXpwEDBigpKUmpqakaO3asPvvss6htJk+eLJ/PFzUGDRoU16YBAE2DpxAqKirStGnTtGPHDhUUFOjUqVPKzs5WVVVV1HYjR47UkSNHImPjxo1xbRoA0DS08rLxu+++G/V4+fLlSk1N1a5du3TTTTdFlvv9fqWnp8enQwBAk3VJ7wmFQiFJUkpKStTyLVu2KDU1Vd27d9e9996r8vLy8z5HdXW1wuFw1AAANA8+55yLpdA5pzFjxujYsWP64IMPIsvz8/N11VVXKTMzUyUlJXr00Ud16tQp7dq1S36/v9bz5ObmasGCBbH/BACABikUCik5Obn+jVyMpk6d6jIzM11paWm92x0+fNi1bt3avfHGG3WuP3nypAuFQpFRWlrqJDEYDAajkY9QKHTBLPH0ntA5M2bM0IYNG7R161Z16tSp3m2DwaAyMzO1b9++Otf7/f46Z0gAgKbPUwg55zRjxgy9+eab2rJli7Kysi5YU1FRodLSUgWDwZibBAA0TZ4uTJg2bZpWrVqlNWvWKCkpSWVlZSorK9OJEyckSV9++aVmz56t7du368CBA9qyZYtGjx6t9u3b67bbbkvIDwAAaMS8vA+k87zut3z5cuecc8ePH3fZ2dmuQ4cOrnXr1q5Lly5u0qRJ7uDBgxe9j1AoZP46JoPBYDAufVzMe0IxXx2XKOFwWIFAwLoNAMAlupir47h3HADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADATIMLIeecdQsAgDi4mN/nDS6EKisrrVsAAMTBxfw+97kGNvU4c+aMDh8+rKSkJPl8vqh14XBYnTt3VmlpqZKTk406tMdxOIvjcBbH4SyOw1kN4Tg451RZWamMjAy1aFH/XKfVZerporVo0UKdOnWqd5vk5ORmfZKdw3E4i+NwFsfhLI7DWdbHIRAIXNR2De7lOABA80EIAQDMNKoQ8vv9mj9/vvx+v3UrpjgOZ3EczuI4nMVxOKuxHYcGd2ECAKD5aFQzIQBA00IIAQDMEEIAADOEEADADCEEADDTqEJo6dKlysrK0hVXXKF+/frpgw8+sG7pssrNzZXP54sa6enp1m0l3NatWzV69GhlZGTI5/Np/fr1Ueudc8rNzVVGRobatm2rYcOGae/evTbNJtCFjsPkyZNrnR+DBg2yaTZB8vLyNGDAACUlJSk1NVVjx47VZ599FrVNczgfLuY4NJbzodGEUH5+vmbOnKm5c+dq9+7d+t73vqecnBwdPHjQurXLqkePHjpy5Ehk7Nmzx7qlhKuqqlKfPn20ZMmSOtcvWrRIixcv1pIlS1RcXKz09HTdcsstTe5muBc6DpI0cuTIqPNj48aNl7HDxCsqKtK0adO0Y8cOFRQU6NSpU8rOzlZVVVVkm+ZwPlzMcZAayfngGokbbrjB3XfffVHLrr32Wvfwww8bdXT5zZ8/3/Xp08e6DVOS3Jtvvhl5fObMGZeenu6efvrpyLKTJ0+6QCDgXnzxRYMOL4+vHwfnnJs0aZIbM2aMST9WysvLnSRXVFTknGu+58PXj4Nzjed8aBQzoZqaGu3atUvZ2dlRy7Ozs7Vt2zajrmzs27dPGRkZysrK0p133qnPP//cuiVTJSUlKisrizo3/H6/hg4d2uzODUnasmWLUlNT1b17d917770qLy+3bimhQqGQJCklJUVS8z0fvn4czmkM50OjCKGjR4/q9OnTSktLi1qelpamsrIyo64uv4EDB2rlypXatGmTXnnlFZWVlWnIkCGqqKiwbs3Mub//5n5uSFJOTo5Wr16twsJCPfvssyouLtaIESNUXV1t3VpCOOc0a9Ys3XjjjerZs6ek5nk+1HUcpMZzPjS4r3Koz9e/X8g5V2tZU5aTkxP5c69evTR48GB17dpVK1as0KxZsww7s9fczw1JGj9+fOTPPXv2VP/+/ZWZmal33nlH48aNM+wsMaZPn66PP/5Yf/3rX2uta07nw/mOQ2M5HxrFTKh9+/Zq2bJlrf/JlJeX1/ofT3PSrl079erVS/v27bNuxcy5qwM5N2oLBoPKzMxskufHjBkztGHDBm3evDnq+8ea2/lwvuNQl4Z6PjSKEGrTpo369eungoKCqOUFBQUaMmSIUVf2qqur9emnnyoYDFq3YiYrK0vp6elR50ZNTY2Kioqa9bkhSRUVFSotLW1S54dzTtOnT9e6detUWFiorKysqPXN5Xy40HGoS4M9HwwvivBk7dq1rnXr1u7VV191//jHP9zMmTNdu3bt3IEDB6xbu2weeOABt2XLFvf555+7HTt2uFGjRrmkpKQmfwwqKyvd7t273e7du50kt3jxYrd79273r3/9yznn3NNPP+0CgYBbt26d27Nnj5swYYILBoMuHA4bdx5f9R2HyspK98ADD7ht27a5kpISt3nzZjd48GDXsWPHJnUcfvazn7lAIOC2bNnijhw5EhnHjx+PbNMczocLHYfGdD40mhByzrnf/OY3LjMz07Vp08Zdf/31UZcjNgfjx493wWDQtW7d2mVkZLhx48a5vXv3WreVcJs3b3aSao1JkyY5585eljt//nyXnp7u/H6/u+mmm9yePXtsm06A+o7D8ePHXXZ2tuvQoYNr3bq169Kli5s0aZI7ePCgddtxVdfPL8ktX748sk1zOB8udBwa0/nA9wkBAMw0iveEAABNEyEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDM/D+kg65sSSVt8QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-28T05:24:15.634336Z",
     "start_time": "2025-10-28T05:24:15.419351Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 显示3张数字6的图片\n",
    "for i in range(35):\n",
    "    if train_dataset.targets[i] == 6:\n",
    "        plt.imshow(train_dataset.data[i], cmap='gray')\n",
    "        plt.show()"
   ],
   "id": "1226b2fdbe693ffc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
